agentrelay 0.1.1

Framework-agnostic transactional agent runtime with idempotent tool calls, saga compensations, and deterministic replay.

AgentRelay

AgentRelay is a framework-agnostic, transactional runtime for AI agents and tool-based workflows.

It gives you:

• Tool-call idempotency – prevent duplicate side effects (emails, payments, DB writes) even when your code retries.
• Saga-style compensations – register compensating tools that run automatically on failure to roll back partial work.
• Deterministic replay – re-run an agent workflow using recorded tool outputs instead of calling external systems again.
• Framework-agnostic SDK – plug into any LLM / agent stack (OpenAI, Gemini, your own code) using a small Python SDK.
• SQL-backed durability – store runs and tool calls in Postgres or MySQL with a simple schema.

AgentRelay is designed for “production-style” agent workflows in domains like fintech, healthcare, and operations, where you care about not double-charging users, not sending emails twice, and being able to debug and audit what an agent actually did.

---

Features

• Idempotent tool calls

  • Each tool invocation is assigned a deterministic idempotency key based on tool name, phase, and arguments.
  • A unique index at the DB layer enforces “do not run the same tool call twice for a given run”.
  • If the same call is retried, AgentRelay returns the previously persisted output instead of re-invoking the tool.

• Saga-style compensations

  • Tools can register a corresponding “compensation” tool.
  • On failure, AgentRelay walks executed steps in reverse order and triggers compensation calls.
  • Best-effort reversals: compensation failures are logged but do not crash the process again.

• Deterministic replay

  • You can replay a past run by opening a session in replay mode.
  • Forward-phase tool calls are served from the tool_calls table instead of calling external APIs or LLMs again.
  • This makes debugging and auditing easier and avoids re-running side effects.

• Framework-agnostic

  • AgentRelay does not depend on any specific LLM or agent framework.
  • You bring your own agent code and LLM client (OpenAI, Gemini, etc.).
  • AgentRelay just wraps tool calls and persists the workflow state.

---

Installation

Once published to PyPI:

pip install agentrelay

---

Coming soon:

• Documentation
• An online dashboard for effective debugging
• A cloud version (so no need of setup on your end, just call the library with the API and get going!)